Ranking Drug Harms in the UK

Harm to others vs harm to users • weighted scores • UK, 2010 • small panel shows total harm

MakeoverMonday
Data Visualization
R Programming
2025
Analysis of the groundbreaking 2010 Lancet study that challenged conventional drug policy by ranking 20 drugs based on evidence rather than legal status. Using multicriteria decision analysis across 16 harm factors, researchers found alcohol causes the most overall harm despite being legal, while many criminalized substances show relatively low harm scores.
Published

September 2, 2025

Original

The original visualization Global meat production, 1961 to 2023 comes from Food and Agriculture Organization of the United Nations (2025)

Original visualization

Makeover

Figure 1: Horizontal diverging bar chart ranking UK drug harms. Alcohol leads with the highest total harm (72 points), showing massive social harm (46.1) but moderate user harm (25.8). Heroin and crack cocaine follow, with higher user harm but lower social impact. Chart reveals disconnect between legal status and evidence-based harm - legal drugs, alcohol, and tobacco rank high, while many illegal drugs like LSD and mushrooms show minimal harm scores. Based on a 2010 Lancet study using multicriteria analysis of 16 harm factors.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
```{r}
#| label: load
#| warning: false
#| message: false
#| results: "hide"

## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
  if (!require("pacman")) install.packages("pacman")
  pacman::p_load(
  tidyverse,  # Easily Install and Load the 'Tidyverse'
  ggtext,     # Improved Text Rendering Support for 'ggplot2'
  showtext,   # Using Fonts More Easily in R Graphs
  scales,     # Scale Functions for Visualization
  glue,       # Interpreted String Literals
  patchwork   # The Composer of Plots 
  )
})

### |- figure size ----
camcorder::gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 10,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))
```

2. Read in the Data

Show code
```{r}
#| label: read
#| include: true
#| eval: true
#| warning: false
#|
drug_harm_raw <- read_csv(
  here::here('data/MakeoverMonday/2025/Drug Harm MCDA - drug_harms_mcda.csv')) |> 
  janitor::clean_names()
```

3. Examine the Data

Show code
```{r}
#| label: examine
#| include: true
#| eval: true
#| results: 'hide'
#| warning: false

glimpse(drug_harm_raw)
skimr::skim_without_charts(drug_harm_raw)
```

4. Tidy Data

Show code
```{r}
#| label: tidy
#| warning: false

drug_totals_actual <- drug_harm_raw |>
  filter(group %in% c("Harm to users", "Harm to others")) |>
  select(-c(category, criteria)) |>
  pivot_longer(cols = -group, names_to = "drug", values_to = "harm_score") |>
  group_by(drug, group) |>
  summarise(harm = sum(harm_score, na.rm = TRUE), .groups = "drop") |>
  pivot_wider(names_from = group, values_from = harm) |>
  janitor::clean_names() |>
  mutate(total_harm = harm_to_users + harm_to_others) |>
  arrange(desc(total_harm))

# Prepare data for diverging bars
diverging_data <- drug_totals_actual |>
  mutate(
    drug_clean = case_when(
      drug == "crack" ~ "Crack cocaine",
      drug == "methylamphet" ~ "Methamphetamine",
      drug == "ghb" ~ "GHB",
      drug == "anabolic_steroids" ~ "Anabolic steroids",
      drug == "lsd" ~ "LSD",
      TRUE ~ str_to_title(drug)
    ),
    harm_to_others_neg = -harm_to_others,
    drug_clean = fct_reorder(drug_clean, total_harm)
  ) |>
  select(drug_clean, harm_to_users, harm_to_others_neg) |>
  pivot_longer(
    cols = c(harm_to_users, harm_to_others_neg),
    names_to = "harm_type", values_to = "harm_value"
  ) |>
  mutate(
    harm_type = case_when(
      harm_type == "harm_to_users" ~ "Harm to users",
      harm_type == "harm_to_others_neg" ~ "Harm to others"
    )
  )
```

5. Visualization Parameters

Show code
```{r}
#| label: params
#| include: true
#| warning: false

### |-  plot aesthetics ----
# Get base colors with custom palette
colors <- get_theme_colors(palette = c(
  "Harm to users" = "#2E6F77",      
  "Harm to others" = "#D97B66"     
))   

### |-  titles and caption ----
title_text <- str_glue("Ranking Drug Harms in the UK")

subtitle_text <- str_glue(
  "**Harm scores** based on a multi-criteria decision analysis by a panel of experts,<br>",
  "ranking drugs from 0 to 100 on **16 distinct criteria**.<br><br>",
  "Harm to others (left) vs harm to users (right) • weighted scores • UK, 2010 • small panel shows total harm"
)

# Create caption
caption_text <- create_mm_caption(
  mm_year = current_year,
  mm_week = current_week,
  source_text = paste0("Nutt et al. (2010),<br>The Lancet – Drug harms in the UK: a multicriteria decision analysis")
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    plot.title = element_text(
      size = rel(1.4), family = fonts$title, face = "bold",
      color = colors$title, lineheight = 1.1, hjust = 0,
      margin = margin(t = 5, b = 10)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9), family = fonts$subtitle, face = "italic",
      color = alpha(colors$subtitle, 0.9), lineheight = 1.1,
      margin = margin(t = 0, b = 20)
    ),

    # Legend formatting
    legend.position = "plot",
    legend.justification = "top",
    legend.margin = margin(l = 12, b = 5),
    legend.key.size = unit(0.8, "cm"),
    legend.box.margin = margin(b = 10),
    legend.title = element_text(face = "bold"),

    # Axis formatting
    axis.ticks.y = element_blank(),
    axis.ticks.x = element_line(color = "gray", linewidth = 0.5),
    # axis.ticks.length = unit(0.2, "cm"),
    axis.title.x = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(t = 10)
    ),
    axis.title.y = element_text(
      face = "bold", size = rel(0.85),
      margin = margin(r = 10)
    ),
    axis.text.x = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),
    axis.text.y = element_text(
      size = rel(0.85), family = fonts$subtitle,
      color = colors$text
    ),

    # Grid lines
    panel.grid.minor = element_line(color = "#ecf0f1", linewidth = 0.2),
    panel.grid.major = element_line(color = "#ecf0f1", linewidth = 0.4),

    # Margin
    plot.margin = margin(20, 20, 20, 20)
  )
)

# Set theme
theme_set(weekly_theme)
```

6. Plot

Show code
```{r}
#| label: plot
#| warning: false

### |- p1: diverging bar chart ----
p1 <- ggplot(diverging_data, aes(x = drug_clean, y = harm_value, fill = harm_type)) +
  # Geoms
  geom_col(width = 0.7) +
  geom_hline(yintercept = 0, color = "black", linewidth = 0.5) +
  geom_text(
    data = diverging_data %>% filter(harm_value > 0), aes(label = round(harm_value, 1)),
    hjust = -0.5,
    size = 3
  ) +
  geom_text(
    data = diverging_data %>% filter(harm_value < 0), aes(label = abs(round(harm_value, 1))),
    hjust = 1.5,
    size = 3
  ) +
  # Annotations
  annotate("text",
    x = 1.5, y = 15, label = "Harm to users →",
    hjust = 0, color = colors$palette[1], fontface = "bold", size = 4
  ) +
  annotate("text",
    x = 1.5, y = -15, label = "← Harm to others",
    hjust = 1, color = colors$palette[2], fontface = "bold", size = 4
  ) +
  # Scales
  scale_fill_manual(values = colors$palette) +
  scale_y_continuous(
    labels = abs,
    breaks = pretty(c(-50, 40), n = 10),
    limits = c(-50, 40)
  ) +
  coord_flip(clip = "off") +
  # Labs
  labs(
    x = NULL,
    y = "Harm Score",
    fill = NULL
  ) +
  # Theme
  theme(
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank(),
    axis.ticks.x = element_blank(),
    # bold top-3
    axis.text.y = element_text(face = ifelse(levels(diverging_data$drug_clean) %in%
      tail(levels(diverging_data$drug_clean), 3),
    "bold", "plain"
    )),
    axis.text.x = element_blank()
  )

### |- p2: total harm bar chart ----
p_total <- ggplot(drug_totals_actual, aes(x = fct_reorder(drug, total_harm, .desc = FALSE), y = total_harm)) +
  # Geoms
  geom_col(width = 0.7, fill = "gray50") +
  geom_text(aes(label = round(total_harm, 1), y = total_harm + 2), hjust = 0, size = 3) +
  # Scales
  scale_y_continuous(
    limits = c(0, max(drug_totals_actual$total_harm) + 5)
  ) +
  coord_flip(clip = "off") +
  # Labs
  labs(
    x = NULL,
    y = "Total Harm"
  ) +
  # Theme
  theme(
    axis.title = element_blank(),
    axis.text.x = element_blank(),
    axis.text.y = element_blank(),
    axis.ticks.x = element_blank(),
    panel.grid.minor = element_blank(),
    panel.grid.major = element_blank(),
    plot.margin = margin(l = -10, r = 5)
  )

### |- final combined plot ----
combined_plots <- p1 + p_total +
  plot_layout(widths = c(2, 0.5))

combined_plots <- combined_plots +
  plot_annotation(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    theme = theme(
      plot.title = element_text(
        size = rel(1.8),
        family = fonts$title,
        face = "bold",
        color = colors$title,
        lineheight = 1.1,
        margin = margin(t = 5, b = 5)
      ),
      plot.subtitle = element_markdown(
        size = rel(0.95),
        family = fonts$subtitle,
        color = alpha(colors$subtitle, 0.9),
        lineheight = 1.2,
        margin = margin(t = 5, b = 10)
      ),
      plot.caption = element_markdown(
        size = rel(0.6),
        family = fonts$caption,
        color = colors$caption,
        hjust = 0.5,
        margin = margin(t = 05)
      )
    )
  )
```

7. Save

Show code
```{r}
#| label: save
#| warning: false

### |-  plot image ----  
save_plot_patchwork(
  plot = combined_plots, 
  type = "makeovermonday", 
  year = current_year,
  week = current_week,
  width = 10, 
  height = 8
  )
```

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      patchwork_1.3.0 glue_1.8.0      scales_1.3.0   
 [5] showtext_0.9-7  showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2   
 [9] lubridate_1.9.3 forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4    
[13] purrr_1.0.2     readr_2.1.5     tidyr_1.3.1     tibble_3.2.1   
[17] ggplot2_3.5.1   tidyverse_2.0.0 pacman_0.5.1   

loaded via a namespace (and not attached):
 [1] gtable_0.3.6       xfun_0.49          htmlwidgets_1.6.4  tzdb_0.5.0        
 [5] yulab.utils_0.1.8  vctrs_0.6.5        tools_4.4.0        generics_0.1.3    
 [9] curl_6.0.0         parallel_4.4.0     gifski_1.32.0-1    fansi_1.0.6       
[13] pkgconfig_2.0.3    ggplotify_0.1.2    skimr_2.1.5        lifecycle_1.0.4   
[17] compiler_4.4.0     farver_2.1.2       munsell_0.5.1      repr_1.1.7        
[21] janitor_2.2.0      codetools_0.2-20   snakecase_0.11.1   htmltools_0.5.8.1 
[25] yaml_2.3.10        crayon_1.5.3       pillar_1.9.0       camcorder_0.1.0   
[29] magick_2.8.5       commonmark_1.9.2   tidyselect_1.2.1   digest_0.6.37     
[33] stringi_1.8.4      labeling_0.4.3     rsvg_2.6.1         rprojroot_2.0.4   
[37] fastmap_1.2.0      grid_4.4.0         colorspace_2.1-1   cli_3.6.4         
[41] magrittr_2.0.3     base64enc_0.1-3    utf8_1.2.4         withr_3.0.2       
[45] bit64_4.5.2        timechange_0.3.0   rmarkdown_2.29     bit_4.5.0         
[49] hms_1.1.3          evaluate_1.0.1     knitr_1.49         markdown_1.13     
[53] gridGraphics_0.5-1 rlang_1.1.6        gridtext_0.1.5     Rcpp_1.0.13-1     
[57] xml2_1.3.6         renv_1.0.3         svglite_2.1.3      rstudioapi_0.17.1 
[61] vroom_1.6.5        jsonlite_1.8.9     R6_2.5.1           fs_1.6.5          
[65] systemfonts_1.1.0

9. GitHub Repository

The complete code for this analysis is available in mm_2025_36.qmd.

For the full repository, click here.

10. References

  1. Data:
  1. Article
Back to top